Digital communication receivers must sample an analog waveform and then reliably detect the sampled data. Signals arriving at a receiver are typically corrupted by intersymbol interference (ISI), crosstalk, echo, and other noise. In order to compensate for such channel distortions, communication receivers often employ well-known equalization techniques. For example, zero equalization or decision-feedback equalization (DFE) techniques (or both) are often employed. Such equalization techniques are widely-used for removing intersymbol interference and to improve the noise margin. See, for example, R. Gitlin et al., Digital Communication Principles, (Plenum Press, 1992) and E. A. Lee and D. G. Messerschmitt, Digital Communications, (Kluwer Academic Press, 1988), each incorporated by reference herein. Generally, zero equalization techniques equalize the pre-cursors of the channel impulse response and decision-feedback equalization equalizes the past cursors of the channel impulse response.
In one typical DFE implementation, a received signal is sampled and compared to one or more thresholds to generate the detected data. A DFE correction is applied in a feedback fashion to produce a DFE corrected signal. The addition/subtraction, however, is considered to be a computationally expensive operation. Thus, a variation of the classical DFE technique, often referred to as Spatial DFE, eliminates the analog adder operation by sampling the received signal using two (or more) vertical slicers that are offset from the common mode voltage. The two slicers are positioned based on the results of a well-known Least Mean Square (LMS) algorithm. One slicer is used for transitions from a binary value of 0 and the second slicer is used for transitions from a binary value of 1. The value of the previous detected bit is used to determine which slicer to use for detection of the current bit. For a more detailed discussion of Spatial DFE techniques, see, for example, Yang and Wu, “High-Performance Adaptive Decision Feedback Equalizer Based on Predictive Parallel Branch Slicer Scheme,” IEEE Signal Processing Systems 2002, 121-26 (2002), incorporated by reference herein. The offset position of the vertical slicers has been determined by evaluating an error term for a known receive data stream and adjusting the offset position using the well-known Least Mean Square algorithm. Such techniques, however, have been found to be unstable in a fixed point highly quantized signal environment and require excessive time to converge.
A need therefore exists for improved methods and apparatus for determining the desired offset position for the vertical slicers. A further need exists for methods and apparatus for determining the desired offset position for the vertical slicers based on an evaluation of the incoming data eye.